Overview

Dataset statistics

Number of variables21
Number of observations41164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory201.7 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

pdays is highly overall correlated with previous and 1 other fieldsHigh correlation
previous is highly overall correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
cons.price.idx is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
cons.conf.idx is highly overall correlated with monthHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 3 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
contact is highly overall correlated with cons.price.idx and 2 other fieldsHigh correlation
month is highly overall correlated with emp.var.rate and 5 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 1 other fieldsHigh correlation
default is highly imbalanced (53.3%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (56.8%)Imbalance
previous has 35539 (86.3%) zerosZeros

Reproduction

Analysis started2023-08-06 09:33:47.673382
Analysis finished2023-08-06 09:34:23.271210
Duration35.6 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02354
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:23.442931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.420109
Coefficient of variation (CV)0.26034952
Kurtosis0.79091477
Mean40.02354
Median Absolute Deviation (MAD)7
Skewness0.78442356
Sum1647529
Variance108.57868
MonotonicityNot monotonic
2023-08-06T09:34:23.762501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1947
 
4.7%
32 1844
 
4.5%
33 1833
 
4.5%
36 1778
 
4.3%
35 1757
 
4.3%
34 1745
 
4.2%
30 1714
 
4.2%
37 1475
 
3.6%
29 1453
 
3.5%
39 1428
 
3.5%
Other values (68) 24190
58.8%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 28
 
0.1%
19 42
 
0.1%
20 65
 
0.2%
21 102
 
0.2%
22 137
 
0.3%
23 226
 
0.5%
24 461
1.1%
25 598
1.5%
26 698
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 22
0.1%
87 1
 
< 0.1%
86 8
 
< 0.1%
85 15
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
admin.
10416 
blue-collar
9252 
technician
6735 
services
3965 
management
2924 
Other values (7)
7872 

Length

Max length13
Median length12
Mean length8.9556409
Min length6

Characters and Unicode

Total characters368650
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 10416
25.3%
blue-collar 9252
22.5%
technician 6735
16.4%
services 3965
 
9.6%
management 2924
 
7.1%
retired 1716
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Length

2023-08-06T09:34:24.083002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 10416
25.3%
blue-collar 9252
22.5%
technician 6735
16.4%
services 3965
 
9.6%
management 2924
 
7.1%
retired 1716
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 47247
12.8%
n 35525
 
9.6%
a 33311
 
9.0%
l 31612
 
8.6%
i 30627
 
8.3%
c 26687
 
7.2%
r 21017
 
5.7%
m 19759
 
5.4%
d 16502
 
4.5%
t 14581
 
4.0%
Other values (14) 91782
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 347561
94.3%
Dash Punctuation 10673
 
2.9%
Other Punctuation 10416
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 47247
13.6%
n 35525
10.2%
a 33311
9.6%
l 31612
9.1%
i 30627
8.8%
c 26687
 
7.7%
r 21017
 
6.0%
m 19759
 
5.7%
d 16502
 
4.7%
t 14581
 
4.2%
Other values (12) 70693
20.3%
Dash Punctuation
ValueCountFrequency (%)
- 10673
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 347561
94.3%
Common 21089
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 47247
13.6%
n 35525
10.2%
a 33311
9.6%
l 31612
9.1%
i 30627
8.8%
c 26687
 
7.7%
r 21017
 
6.0%
m 19759
 
5.7%
d 16502
 
4.7%
t 14581
 
4.2%
Other values (12) 70693
20.3%
Common
ValueCountFrequency (%)
- 10673
50.6%
. 10416
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 47247
12.8%
n 35525
 
9.6%
a 33311
 
9.0%
l 31612
 
8.6%
i 30627
 
8.3%
c 26687
 
7.2%
r 21017
 
5.7%
m 19759
 
5.4%
d 16502
 
4.5%
t 14581
 
4.0%
Other values (14) 91782
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
married
24914 
single
11560 
divorced
4610 
unknown
 
80

Length

Max length8
Median length7
Mean length6.8311632
Min length6

Characters and Unicode

Total characters281198
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 24914
60.5%
single 11560
28.1%
divorced 4610
 
11.2%
unknown 80
 
0.2%

Length

2023-08-06T09:34:24.369091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:24.681154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 24914
60.5%
single 11560
28.1%
divorced 4610
 
11.2%
unknown 80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 54438
19.4%
i 41084
14.6%
e 41084
14.6%
d 34134
12.1%
m 24914
8.9%
a 24914
8.9%
n 11800
 
4.2%
s 11560
 
4.1%
g 11560
 
4.1%
l 11560
 
4.1%
Other values (6) 14150
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 281198
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 54438
19.4%
i 41084
14.6%
e 41084
14.6%
d 34134
12.1%
m 24914
8.9%
a 24914
8.9%
n 11800
 
4.2%
s 11560
 
4.1%
g 11560
 
4.1%
l 11560
 
4.1%
Other values (6) 14150
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281198
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 54438
19.4%
i 41084
14.6%
e 41084
14.6%
d 34134
12.1%
m 24914
8.9%
a 24914
8.9%
n 11800
 
4.2%
s 11560
 
4.1%
g 11560
 
4.1%
l 11560
 
4.1%
Other values (6) 14150
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 54438
19.4%
i 41084
14.6%
e 41084
14.6%
d 34134
12.1%
m 24914
8.9%
a 24914
8.9%
n 11800
 
4.2%
s 11560
 
4.1%
g 11560
 
4.1%
l 11560
 
4.1%
Other values (6) 14150
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
university.degree
12160 
high.school
9509 
basic.9y
6045 
professional.course
5237 
basic.4y
4176 
Other values (3)
4037 

Length

Max length19
Median length17
Mean length12.709965
Min length7

Characters and Unicode

Total characters523193
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree 12160
29.5%
high.school 9509
23.1%
basic.9y 6045
14.7%
professional.course 5237
12.7%
basic.4y 4176
 
10.1%
basic.6y 2290
 
5.6%
unknown 1729
 
4.2%
illiterate 18
 
< 0.1%

Length

2023-08-06T09:34:24.928057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:25.239938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 12160
29.5%
high.school 9509
23.1%
basic.9y 6045
14.7%
professional.course 5237
12.7%
basic.4y 4176
 
10.1%
basic.6y 2290
 
5.6%
unknown 1729
 
4.2%
illiterate 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 59150
 
11.3%
i 51613
 
9.9%
s 49891
 
9.5%
. 39417
 
7.5%
o 36458
 
7.0%
r 34812
 
6.7%
h 28527
 
5.5%
c 27257
 
5.2%
y 24671
 
4.7%
n 22584
 
4.3%
Other values (15) 148813
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 471265
90.1%
Other Punctuation 39417
 
7.5%
Decimal Number 12511
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 59150
12.6%
i 51613
11.0%
s 49891
10.6%
o 36458
 
7.7%
r 34812
 
7.4%
h 28527
 
6.1%
c 27257
 
5.8%
y 24671
 
5.2%
n 22584
 
4.8%
g 21669
 
4.6%
Other values (11) 114633
24.3%
Decimal Number
ValueCountFrequency (%)
9 6045
48.3%
4 4176
33.4%
6 2290
 
18.3%
Other Punctuation
ValueCountFrequency (%)
. 39417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 471265
90.1%
Common 51928
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 59150
12.6%
i 51613
11.0%
s 49891
10.6%
o 36458
 
7.7%
r 34812
 
7.4%
h 28527
 
6.1%
c 27257
 
5.8%
y 24671
 
5.2%
n 22584
 
4.8%
g 21669
 
4.6%
Other values (11) 114633
24.3%
Common
ValueCountFrequency (%)
. 39417
75.9%
9 6045
 
11.6%
4 4176
 
8.0%
6 2290
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 523193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 59150
 
11.3%
i 51613
 
9.9%
s 49891
 
9.5%
. 39417
 
7.5%
o 36458
 
7.0%
r 34812
 
6.7%
h 28527
 
5.5%
c 27257
 
5.2%
y 24671
 
4.7%
n 22584
 
4.3%
Other values (15) 148813
28.4%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
no
32566 
unknown
8595 
yes
 
3

Length

Max length7
Median length2
Mean length3.0440676
Min length2

Characters and Unicode

Total characters125306
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 32566
79.1%
unknown 8595
 
20.9%
yes 3
 
< 0.1%

Length

2023-08-06T09:34:25.929651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:26.194988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 32566
79.1%
unknown 8595
 
20.9%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 58351
46.6%
o 41161
32.8%
u 8595
 
6.9%
k 8595
 
6.9%
w 8595
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125306
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 58351
46.6%
o 41161
32.8%
u 8595
 
6.9%
k 8595
 
6.9%
w 8595
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 125306
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 58351
46.6%
o 41161
32.8%
u 8595
 
6.9%
k 8595
 
6.9%
w 8595
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 58351
46.6%
o 41161
32.8%
u 8595
 
6.9%
k 8595
 
6.9%
w 8595
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
yes
21566 
no
18608 
unknown
 
990

Length

Max length7
Median length3
Mean length2.6441551
Min length2

Characters and Unicode

Total characters108844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 21566
52.4%
no 18608
45.2%
unknown 990
 
2.4%

Length

2023-08-06T09:34:26.421002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:26.713261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 21566
52.4%
no 18608
45.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 21578
19.8%
y 21566
19.8%
e 21566
19.8%
s 21566
19.8%
o 19598
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108844
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 21578
19.8%
y 21566
19.8%
e 21566
19.8%
s 21566
19.8%
o 19598
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 108844
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 21578
19.8%
y 21566
19.8%
e 21566
19.8%
s 21566
19.8%
o 19598
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 21578
19.8%
y 21566
19.8%
e 21566
19.8%
s 21566
19.8%
o 19598
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
no
33926 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.2720338
Min length2

Characters and Unicode

Total characters93526
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 33926
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Length

2023-08-06T09:34:26.940821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:27.209571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 33926
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 36896
39.4%
o 34916
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93526
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36896
39.4%
o 34916
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 93526
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36896
39.4%
o 34916
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36896
39.4%
o 34916
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
cellular
26126 
telephone
15038 

Length

Max length9
Median length8
Mean length8.3653192
Min length8

Characters and Unicode

Total characters344350
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 26126
63.5%
telephone 15038
36.5%

Length

2023-08-06T09:34:27.435267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:27.700548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cellular 26126
63.5%
telephone 15038
36.5%

Most occurring characters

ValueCountFrequency (%)
l 93416
27.1%
e 71240
20.7%
c 26126
 
7.6%
u 26126
 
7.6%
a 26126
 
7.6%
r 26126
 
7.6%
t 15038
 
4.4%
p 15038
 
4.4%
h 15038
 
4.4%
o 15038
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 344350
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 93416
27.1%
e 71240
20.7%
c 26126
 
7.6%
u 26126
 
7.6%
a 26126
 
7.6%
r 26126
 
7.6%
t 15038
 
4.4%
p 15038
 
4.4%
h 15038
 
4.4%
o 15038
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 344350
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 93416
27.1%
e 71240
20.7%
c 26126
 
7.6%
u 26126
 
7.6%
a 26126
 
7.6%
r 26126
 
7.6%
t 15038
 
4.4%
p 15038
 
4.4%
h 15038
 
4.4%
o 15038
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 93416
27.1%
e 71240
20.7%
c 26126
 
7.6%
u 26126
 
7.6%
a 26126
 
7.6%
r 26126
 
7.6%
t 15038
 
4.4%
p 15038
 
4.4%
h 15038
 
4.4%
o 15038
 
4.4%

month
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
may
13765 
jul
7164 
aug
6174 
jun
5318 
nov
4099 
Other values (5)
4644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123492
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13765
33.4%
jul 7164
17.4%
aug 6174
15.0%
jun 5318
 
12.9%
nov 4099
 
10.0%
apr 2630
 
6.4%
oct 716
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Length

2023-08-06T09:34:27.934898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:28.238988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
may 13765
33.4%
jul 7164
17.4%
aug 6174
15.0%
jun 5318
 
12.9%
nov 4099
 
10.0%
apr 2630
 
6.4%
oct 716
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 23115
18.7%
u 18656
15.1%
m 14311
11.6%
y 13765
11.1%
j 12482
10.1%
n 9417
7.6%
l 7164
 
5.8%
g 6174
 
5.0%
o 4815
 
3.9%
v 4099
 
3.3%
Other values (7) 9494
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123492
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23115
18.7%
u 18656
15.1%
m 14311
11.6%
y 13765
11.1%
j 12482
10.1%
n 9417
7.6%
l 7164
 
5.8%
g 6174
 
5.0%
o 4815
 
3.9%
v 4099
 
3.3%
Other values (7) 9494
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 123492
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23115
18.7%
u 18656
15.1%
m 14311
11.6%
y 13765
11.1%
j 12482
10.1%
n 9417
7.6%
l 7164
 
5.8%
g 6174
 
5.0%
o 4815
 
3.9%
v 4099
 
3.3%
Other values (7) 9494
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 23115
18.7%
u 18656
15.1%
m 14311
11.6%
y 13765
11.1%
j 12482
10.1%
n 9417
7.6%
l 7164
 
5.8%
g 6174
 
5.0%
o 4815
 
3.9%
v 4099
 
3.3%
Other values (7) 9494
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
thu
8613 
mon
8510 
wed
8134 
tue
8082 
fri
7825 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123492
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu 8613
20.9%
mon 8510
20.7%
wed 8134
19.8%
tue 8082
19.6%
fri 7825
19.0%

Length

2023-08-06T09:34:28.534100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:28.815336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
thu 8613
20.9%
mon 8510
20.7%
wed 8134
19.8%
tue 8082
19.6%
fri 7825
19.0%

Most occurring characters

ValueCountFrequency (%)
t 16695
13.5%
u 16695
13.5%
e 16216
13.1%
h 8613
7.0%
m 8510
6.9%
o 8510
6.9%
n 8510
6.9%
w 8134
6.6%
d 8134
6.6%
f 7825
6.3%
Other values (2) 15650
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123492
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 16695
13.5%
u 16695
13.5%
e 16216
13.1%
h 8613
7.0%
m 8510
6.9%
o 8510
6.9%
n 8510
6.9%
w 8134
6.6%
d 8134
6.6%
f 7825
6.3%
Other values (2) 15650
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 123492
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 16695
13.5%
u 16695
13.5%
e 16216
13.1%
h 8613
7.0%
m 8510
6.9%
o 8510
6.9%
n 8510
6.9%
w 8134
6.6%
d 8134
6.6%
f 7825
6.3%
Other values (2) 15650
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 16695
13.5%
u 16695
13.5%
e 16216
13.1%
h 8613
7.0%
m 8510
6.9%
o 8510
6.9%
n 8510
6.9%
w 8134
6.6%
d 8134
6.6%
f 7825
6.3%
Other values (2) 15650
12.7%

duration
Real number (ℝ)

Distinct1544
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.34664
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:29.097595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.3314
Coefficient of variation (CV)1.0038118
Kurtosis20.239603
Mean258.34664
Median Absolute Deviation (MAD)94
Skewness3.2624737
Sum10634581
Variance67252.776
MonotonicityNot monotonic
2023-08-06T09:34:29.403900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 170
 
0.4%
85 170
 
0.4%
136 168
 
0.4%
73 167
 
0.4%
87 162
 
0.4%
124 162
 
0.4%
72 161
 
0.4%
104 161
 
0.4%
111 160
 
0.4%
106 159
 
0.4%
Other values (1534) 39524
96.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 12
 
< 0.1%
5 30
 
0.1%
6 37
0.1%
7 54
0.1%
8 69
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
4199 1
< 0.1%
3785 1
< 0.1%
3643 1
< 0.1%
3631 1
< 0.1%
3509 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%

campaign
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5681664
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:29.691816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7706232
Coefficient of variation (CV)1.0788332
Kurtosis36.963919
Mean2.5681664
Median Absolute Deviation (MAD)1
Skewness4.7615813
Sum105716
Variance7.6763532
MonotonicityNot monotonic
2023-08-06T09:34:29.963440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 17626
42.8%
2 10566
25.7%
3 5339
 
13.0%
4 2649
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
Other values (32) 869
 
2.1%
ValueCountFrequency (%)
1 17626
42.8%
2 10566
25.7%
3 5339
 
13.0%
4 2649
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%

pdays
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.45416
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:30.247591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.96331
Coefficient of variation (CV)0.19425684
Kurtosis22.213643
Mean962.45416
Median Absolute Deviation (MAD)0
Skewness-4.9205827
Sum39618463
Variance34955.278
MonotonicityNot monotonic
2023-08-06T09:34:30.582421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
999 39649
96.3%
3 439
 
1.1%
6 412
 
1.0%
4 118
 
0.3%
9 64
 
0.2%
2 61
 
0.1%
7 60
 
0.1%
12 58
 
0.1%
10 52
 
0.1%
5 46
 
0.1%
Other values (17) 205
 
0.5%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 26
 
0.1%
2 61
 
0.1%
3 439
1.1%
4 118
 
0.3%
5 46
 
0.1%
6 412
1.0%
7 60
 
0.1%
8 18
 
< 0.1%
9 64
 
0.2%
ValueCountFrequency (%)
999 39649
96.3%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
19 3
 
< 0.1%
18 7
 
< 0.1%
17 8
 
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17306384
Minimum0
Maximum7
Zeros35539
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:30.974107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49502771
Coefficient of variation (CV)2.8603763
Kurtosis20.095511
Mean0.17306384
Median Absolute Deviation (MAD)0
Skewness3.8307487
Sum7124
Variance0.24505243
MonotonicityNot monotonic
2023-08-06T09:34:31.420197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35539
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 35539
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 5
 
< 0.1%
5 18
 
< 0.1%
4 70
 
0.2%
3 216
 
0.5%
2 754
 
1.8%
1 4561
 
11.1%
0 35539
86.3%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
nonexistent
35539 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.453406
Min length7

Characters and Unicode

Total characters430304
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 35539
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Length

2023-08-06T09:34:31.931078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-06T09:34:32.461875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 35539
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 106617
24.8%
e 76703
17.8%
t 71078
16.5%
i 39791
 
9.2%
s 39658
 
9.2%
o 35539
 
8.3%
x 35539
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 430304
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 106617
24.8%
e 76703
17.8%
t 71078
16.5%
i 39791
 
9.2%
s 39658
 
9.2%
o 35539
 
8.3%
x 35539
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 430304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 106617
24.8%
e 76703
17.8%
t 71078
16.5%
i 39791
 
9.2%
s 39658
 
9.2%
o 35539
 
8.3%
x 35539
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 106617
24.8%
e 76703
17.8%
t 71078
16.5%
i 39791
 
9.2%
s 39658
 
9.2%
o 35539
 
8.3%
x 35539
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

emp.var.rate
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081957536
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17181
Negative (%)41.7%
Memory size1.6 MiB
2023-08-06T09:34:32.868785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5708054
Coefficient of variation (CV)19.166089
Kurtosis-1.0627647
Mean0.081957536
Median Absolute Deviation (MAD)0.3
Skewness-0.72402559
Sum3373.7
Variance2.4674297
MonotonicityNot monotonic
2023-08-06T09:34:33.246194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 16222
39.4%
-1.8 9180
22.3%
1.1 7761
18.9%
-0.1 3681
 
8.9%
-2.9 1661
 
4.0%
-3.4 1069
 
2.6%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-3 172
 
0.4%
-0.2 10
 
< 0.1%
ValueCountFrequency (%)
-3.4 1069
 
2.6%
-3 172
 
0.4%
-2.9 1661
 
4.0%
-1.8 9180
22.3%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-0.2 10
 
< 0.1%
-0.1 3681
 
8.9%
1.1 7761
18.9%
1.4 16222
39.4%
ValueCountFrequency (%)
1.4 16222
39.4%
1.1 7761
18.9%
-0.1 3681
 
8.9%
-0.2 10
 
< 0.1%
-1.1 635
 
1.5%
-1.7 773
 
1.9%
-1.8 9180
22.3%
-2.9 1661
 
4.0%
-3 172
 
0.4%
-3.4 1069
 
2.6%

cons.price.idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.575775
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:33.723839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57883792
Coefficient of variation (CV)0.0061857667
Kurtosis-0.82989364
Mean93.575775
Median Absolute Deviation (MAD)0.38
Skewness-0.23081815
Sum3851953.2
Variance0.33505333
MonotonicityNot monotonic
2023-08-06T09:34:34.123960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 7761
18.9%
93.918 6677
16.2%
92.893 5792
14.1%
93.444 5171
12.6%
94.465 4374
10.6%
93.2 3614
8.8%
93.075 2456
 
6.0%
92.201 770
 
1.9%
92.963 715
 
1.7%
92.431 445
 
1.1%
Other values (16) 3389
8.2%
ValueCountFrequency (%)
92.201 770
 
1.9%
92.379 267
 
0.6%
92.431 445
 
1.1%
92.469 176
 
0.4%
92.649 357
 
0.9%
92.713 172
 
0.4%
92.756 10
 
< 0.1%
92.843 282
 
0.7%
92.893 5792
14.1%
92.963 715
 
1.7%
ValueCountFrequency (%)
94.767 128
 
0.3%
94.601 204
 
0.5%
94.465 4374
10.6%
94.215 311
 
0.8%
94.199 303
 
0.7%
94.055 229
 
0.6%
94.027 233
 
0.6%
93.994 7761
18.9%
93.918 6677
16.2%
93.876 212
 
0.5%

cons.conf.idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.503127
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41164
Negative (%)100.0%
Memory size1.6 MiB
2023-08-06T09:34:34.369201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6275218
Coefficient of variation (CV)-0.11425098
Kurtosis-0.35963671
Mean-40.503127
Median Absolute Deviation (MAD)4.4
Skewness0.30257178
Sum-1667270.7
Variance21.413958
MonotonicityNot monotonic
2023-08-06T09:34:34.621912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 7761
18.9%
-42.7 6677
16.2%
-46.2 5792
14.1%
-36.1 5171
12.6%
-41.8 4374
10.6%
-42 3614
8.8%
-47.1 2456
 
6.0%
-31.4 770
 
1.9%
-40.8 715
 
1.7%
-26.9 445
 
1.1%
Other values (16) 3389
8.2%
ValueCountFrequency (%)
-50.8 128
 
0.3%
-50 282
 
0.7%
-49.5 204
 
0.5%
-47.1 2456
 
6.0%
-46.2 5792
14.1%
-45.9 10
 
< 0.1%
-42.7 6677
16.2%
-42 3614
8.8%
-41.8 4374
10.6%
-40.8 715
 
1.7%
ValueCountFrequency (%)
-26.9 445
 
1.1%
-29.8 267
 
0.6%
-30.1 357
 
0.9%
-31.4 770
 
1.9%
-33 172
 
0.4%
-33.6 176
 
0.4%
-34.6 174
 
0.4%
-34.8 264
 
0.6%
-36.1 5171
12.6%
-36.4 7761
18.9%

euribor3m
Real number (ℝ)

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6212961
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:34.899189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7344266
Coefficient of variation (CV)0.47895189
Kurtosis-1.40678
Mean3.6212961
Median Absolute Deviation (MAD)0.108
Skewness-0.70920047
Sum149067.03
Variance3.0082356
MonotonicityNot monotonic
2023-08-06T09:34:35.206936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2868
 
7.0%
4.962 2609
 
6.3%
4.963 2487
 
6.0%
4.961 1902
 
4.6%
4.856 1210
 
2.9%
4.964 1175
 
2.9%
1.405 1169
 
2.8%
4.965 1069
 
2.6%
4.864 1044
 
2.5%
4.96 1013
 
2.5%
Other values (306) 24618
59.8%
ValueCountFrequency (%)
0.634 8
 
< 0.1%
0.635 43
0.1%
0.636 14
 
< 0.1%
0.637 6
 
< 0.1%
0.638 7
 
< 0.1%
0.639 16
 
< 0.1%
0.64 10
 
< 0.1%
0.642 35
0.1%
0.643 23
0.1%
0.644 38
0.1%
ValueCountFrequency (%)
5.045 9
 
< 0.1%
5 7
 
< 0.1%
4.97 172
 
0.4%
4.968 990
 
2.4%
4.967 643
 
1.6%
4.966 618
 
1.5%
4.965 1069
2.6%
4.964 1175
2.9%
4.963 2487
6.0%
4.962 2609
6.3%

nr.employed
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.0338
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-08-06T09:34:35.467790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.2512
Coefficient of variation (CV)0.01398311
Kurtosis-0.0033188374
Mean5167.0338
Median Absolute Deviation (MAD)37.1
Skewness-1.0443717
Sum2.1269578 × 108
Variance5220.2359
MonotonicityNot monotonic
2023-08-06T09:34:35.694977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 16222
39.4%
5099.1 8530
20.7%
5191 7761
18.9%
5195.8 3681
 
8.9%
5076.2 1661
 
4.0%
5017.5 1069
 
2.6%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
4963.6 635
 
1.5%
5023.5 172
 
0.4%
ValueCountFrequency (%)
4963.6 635
 
1.5%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
5017.5 1069
 
2.6%
5023.5 172
 
0.4%
5076.2 1661
 
4.0%
5099.1 8530
20.7%
5176.3 10
 
< 0.1%
5191 7761
18.9%
5195.8 3681
8.9%
ValueCountFrequency (%)
5228.1 16222
39.4%
5195.8 3681
 
8.9%
5191 7761
18.9%
5176.3 10
 
< 0.1%
5099.1 8530
20.7%
5076.2 1661
 
4.0%
5023.5 172
 
0.4%
5017.5 1069
 
2.6%
5008.7 650
 
1.6%
4991.6 773
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
False
36526 
True
4638 
ValueCountFrequency (%)
False 36526
88.7%
True 4638
 
11.3%
2023-08-06T09:34:35.960999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-08-06T09:34:18.631078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:53.292737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:55.896302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:58.385790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:00.869104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:03.512007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:07.256416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:09.689595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:12.974588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:15.464721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:19.047574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:53.567194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:56.161220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:58.650784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:01.140100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:03.933249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:07.531403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:09.955128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:13.224798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:15.732136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:19.440274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:53.834641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:56.403748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:58.895365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:01.377034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:04.349010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:07.770363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:10.215558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:13.495991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:15.977323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:19.858664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:54.103457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:56.654889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:59.132454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:01.621947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:04.713580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:08.004741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:11.290764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:13.740387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:16.219731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:20.275065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:54.353686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:56.905668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:59.377530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:01.857193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:05.098196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:08.257311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:11.539308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:13.981223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:16.483339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:20.631730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:54.615125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:57.167951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:59.641215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:02.121964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:05.518597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:08.511172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:11.799314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:14.243505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:16.737657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:20.872789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:54.877845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:57.406903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:59.877561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:02.362534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:05.925370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:08.752742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:12.033262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:14.495299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:17.071851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:21.118623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:55.127687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:57.650357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:00.133503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:02.602229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:06.289429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:08.981526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:12.257706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:14.732535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:17.468338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:21.368736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:55.386192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:57.893820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:00.385872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:02.837572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:06.728106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:09.225871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:12.513863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:14.970150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:17.860372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:21.617831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:55.640164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:33:58.135656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:00.631808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:03.099858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:06.994688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:09.451823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:12.746258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:15.209969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-06T09:34:18.242808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-06T09:34:36.186141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedjobmaritaleducationdefaulthousingloancontactmonthday_of_weekpoutcomey
age1.000-0.0020.006-0.001-0.0130.0450.0450.1140.0550.0450.2490.2620.1170.1460.0010.0100.0990.0940.0250.1100.172
duration-0.0021.000-0.081-0.0830.042-0.0690.003-0.009-0.078-0.0950.0060.0000.0000.0000.0000.0000.0320.0200.0080.0170.377
campaign0.006-0.0811.0000.056-0.0880.1560.096-0.0010.1410.1440.0000.0000.0020.0170.0220.0210.0640.0470.0180.0470.052
pdays-0.001-0.0830.0561.000-0.5100.2280.057-0.0770.2790.2910.1400.0420.0550.0800.0080.0000.1180.2400.0120.9520.325
previous-0.0130.042-0.088-0.5101.000-0.435-0.283-0.116-0.455-0.4390.0530.0300.0190.0750.0160.0000.2420.1270.0000.7340.236
emp.var.rate0.045-0.0690.1560.228-0.4351.0000.6650.2250.9400.9450.1350.0680.0660.1570.0520.0120.4620.6590.0350.3800.342
cons.price.idx0.0450.0030.0960.057-0.2830.6651.0000.2460.4910.4650.1310.0690.0980.1540.0690.0170.6750.6760.0500.3860.336
cons.conf.idx0.114-0.009-0.001-0.077-0.1160.2250.2461.0000.2370.1330.1090.0720.0640.1380.0400.0110.4170.6000.0450.3690.386
euribor3m0.055-0.0780.1410.279-0.4550.9400.4910.2371.0000.9290.1280.0680.0600.1590.0520.0120.4690.5520.1370.4180.399
nr.employed0.045-0.0950.1440.291-0.4390.9450.4650.1330.9291.0000.1340.0720.0670.1400.0400.0100.5020.6020.0460.4120.410
job0.2490.0060.0000.1400.0530.1350.1310.1090.1280.1341.0000.1840.3590.1520.0100.0100.1280.1100.0160.1000.152
marital0.2620.0000.0000.0420.0300.0680.0690.0720.0680.0720.1841.0000.1160.0950.0090.0000.0720.0500.0110.0370.054
education0.1170.0000.0020.0550.0190.0660.0980.0640.0600.0670.3590.1161.0000.1700.0130.0000.1230.0950.0200.0420.067
default0.1460.0000.0170.0800.0750.1570.1540.1380.1590.1400.1520.0950.1701.0000.0110.0020.1360.1120.0110.0770.099
housing0.0010.0000.0220.0080.0160.0520.0690.0400.0520.0400.0100.0090.0130.0111.0000.7080.0840.0540.0150.0170.010
loan0.0100.0000.0210.0000.0000.0120.0170.0110.0120.0100.0100.0000.0000.0020.7081.0000.0240.0200.0060.0000.000
contact0.0990.0320.0640.1180.2420.4620.6750.4170.4690.5020.1280.0720.1230.1360.0840.0241.0000.6090.0550.2430.145
month0.0940.0200.0470.2400.1270.6590.6760.6000.5520.6020.1100.0500.0950.1120.0540.0200.6091.0000.0670.2420.274
day_of_week0.0250.0080.0180.0120.0000.0350.0500.0450.1370.0460.0160.0110.0200.0110.0150.0060.0550.0671.0000.0150.023
poutcome0.1100.0170.0470.9520.7340.3800.3860.3690.4180.4120.1000.0370.0420.0770.0170.0000.2430.2420.0151.0000.320
y0.1720.3770.0520.3250.2360.3420.3360.3860.3990.4100.1520.0540.0670.0990.0100.0000.1450.2740.0230.3201.000

Missing values

2023-08-06T09:34:22.065971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-06T09:34:22.852865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056housemaidmarriedbasic.4ynononotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
545servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
659admin.marriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
824techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
925servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
4118036admin.marrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
4118137admin.marrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
4118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
4118446blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
4118644technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no